Computational Genome Analysis: An Introduction (Hardcover)

Richard C. Deonier, Simon Tavaré, Michael S. Waterman

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商品描述

Description

Computational Genome Analysis: An Introduction presents the foundations of key problems in computational molecular biology and bioinformatics. It focuses on computational and statistical principles applied to genomes, and introduces the mathematics and statistics that are crucial for understanding these applications. The book is appropriate for a one-semester course for advanced undergraduate or beginning graduate students, and it can also introduce computational biology to computer scientists, mathematicians, or biologists who are extending their interests into this exciting field.

This book features:

Topics organized around biological problems, such as sequence alignment and assembly, DNA signals, analysis of gene expression, and human genetic variation

Presentation of fundamentals of probability, statistics, and algorithms

Implementation of computational methods with numerous examples based upon the R statistics package

Extensive descriptions and explanations to complement the analytical development

More than 100 illustrations and diagrams (some in color) to reinforce concepts and present key results from the primary literature

Exercises at the end of chapters

Michael S. Waterman is a University Professor, a USC Associates Chair in Natural Sciences, and Professor of Biological Sciences, Computer Science, and Mathematics at the University of Southern California. A member of the National Academy of Sciences and the American Academy of Arts and Sciences, Professor Waterman is Founding Editor and Co-Editor in Chief of the Journal of Computational Biology. His research has focused on computational analysis of molecular sequence data. His best-known work is the co-development of the local alignment Smith-Waterman algorithm, which has become the foundational tool for database search methods. His interests have also encompassed physical mapping, as exemplified by the Lander-Waterman formulas, and genome sequence assembly using an Eulerian path method.

Simon Tavaré holds the George and Louise Kawamoto Chair in Biological Sciences and is a Professor of Biological Sciences, Mathematics, and Preventive Medicine at the University of Southern California. Professor Tavaré's research lies at the interface between statistics and biology, specifically focusing on problems arising in molecular biology, human genetics, population genetics, molecular evolution, and bioinformatics. His statistical interests focus on stochastic computation. Among the applications are linkage disequilibrium mapping, stem cell evolution, and inference in the fossil record. Dr. Tavaré is also a professor in the Department of Oncology at the University of Cambridge, England, where his group concentrates on cancer genomics.

Richard C. Deonier is Professor Emeritus in the Molecular and Computational Biology Section of the Department of Biological Sciences at the University of Southern California. Originally trained as a physical biochemist, His major research has been in areas of molecular genetics, with particular interests in physical methods for gene mapping, bacterial transposable elements, and conjugative plasmids. During 30 years of active teaching, he has taught chemistry, biology, and computational biology at both the undergraduate and graduate levels.

 

Table of contents

Biology in a Nutshell.- Words.- Word Distributions and Occurrences.- Physical Mapping of DNA.- Genome Rearrangements.- Sequence Alignment.- Rapid Alignment Methods: FASTA and BLAST.- DNA Sequence Assembly.- Signals in DNA.- Similarity, Distance, and Clustering.- Measuring Expression of Genome Information.- Inferring the Past: Phylogenetic Trees.- Genetic Variation in Population.- Comparative Genomics.

商品描述(中文翻譯)

《計算基因組分析:入門》介紹了計算分子生物學和生物信息學中關鍵問題的基礎。它專注於應用於基因組的計算和統計原理,並介紹了理解這些應用所必不可少的數學和統計知識。本書適合高年級本科生或初級研究生的一學期課程,也可以向計算機科學家、數學家或生物學家介紹計算生物學這個令人興奮的領域。

本書特點如下:
- 圍繞生物問題組織的主題,如序列對齊和組裝、DNA信號、基因表達分析和人類基因變異
- 提供概率、統計和算法的基礎知識
- 使用基於R統計軟件包的多個示例實現計算方法
- 詳細的描述和解釋以補充分析發展
- 100多個插圖和圖表(部分彩色)以加強概念並呈現來自主要文獻的關鍵結果
- 章末練習題

邁克爾·S·沃特曼(Michael S. Waterman)是南加州大學的大學教授,USC自然科學副教席,並擔任生物科學、計算機科學和數學的教授。作為美國國家科學院和美國藝術與科學院的成員,沃特曼教授是《計算生物學期刊》的創始編輯和聯合主編。他的研究重點是分子序列數據的計算分析。他最著名的工作是共同開發的局部對齊史密斯-沃特曼算法,該算法已成為數據庫搜索方法的基礎工具。他的興趣還包括物理圖譜,如蘭德-沃特曼公式,以及使用歐拉路徑方法進行基因組序列組裝。

西蒙·塔瓦雷(Simon Tavaré)擁有喬治和路易斯·卡瓦莫托(George and Louise Kawamoto)生物科學講座,並擔任南加州大學的生物科學、數學和預防醫學教授。塔瓦雷教授的研究位於統計學和生物學的交界處,專注於分子生物學、人類遺傳學、群體遺傳學、分子進化和生物信息學中出現的問題。他的統計學興趣集中在隨機計算上。應用包括連鎖不平衡映射、幹細胞進化和化石記錄推斷。塔瓦雷博士還是英國劍橋大學腫瘤學系的教授,他的研究小組專注於癌症基因組學。

理查德·C·迪奧尼爾(Richard C. Deonier)是南加州大學生物科學系分子和計算生物學部門的名譽教授。他最初接受的是物理生物化學的培訓,他的主要研究領域是分子遺傳學,尤其是基因圖譜的物理方法,細菌可移動元件和共軛質粒。在30年的積極教學中,他在本科和研究生課程中教授化學、生物學和計算生物學。

《目錄》
- 簡介
- 第1章:序列對齊和組裝
- 第2章:DNA信號
- 第3章:基因表達分析
- 第4章:人類基因變異
- 第5章:概率和統計基礎知識
- 第6章:算法基礎知識
- 第7章:R統計軟件包的計算方法實現
- 第8章:附錄:數學和統計基礎知識